Capacity Engineer, Compute
San Francisco, CA | New York City, NY | Seattle, WAFull-TimeMid-levelSoftware Engineering
Skills
About Anthropic
- Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
- The mission of the Compute team is to provide input into our company-wide cloud infrastructure strategy and efficiency deliverables, advise on key decisions affecting budget, and provide capacity planning and performance expertise to various anthropic-wide stakeholders in finance and engineering leadership. As an early member of this team, you would be required to work with engineering teams to ensure optimal operation and growth of our infrastructure from both a cost and technology perspective and collaborate cross-functionally with finance and data science partners to analyze and forecast growth.
Responsibilities
- Develop self-service tools and processes to enable anthropic engineers to understand their capacity, efficiency, and costs
- Design, develop, and lead necessary automation to help capacity plan for both near and long term outcomes
- Institute and design governance workflows to help manage additional capacity request approvals
- Investigate new capacity requests to ensure the best use of resources and that instances are sized appropriately
- Build and drive cost to serve analytics programs to guide engineering, finance, and leadership on the total cost (TCO) and infrastructure impact of our scaling factors. Inform pricing conversations through customer profile sensitive gross margin analysis.
- Tech lead with outside vendors to manage anthropic capacity needs
- Proactively identify infrastructure inefficiency opportunities, document proposal and be a key contributor in driving a positive outcome
- Serve as an advisor to engineering and finance functions and executive team for one of the largest areas of expenditure
- Work closely with TPMs on special efficiency projects and help deliver committed outcomes
You may be a good fit if you:
- 5+ years experience in capacity engineering
- 5+ years experience in a technical role
- Intermediate knowledge of various public cloud providers
- Experience with data modeling for public cloud
- Experience with budgeting, capacity planning experience, and cloud efficiency optimization workflows
- Experience in scripting and building automation tools
- Self-disciplined and thrives in fast paced environments
- Excellent communication skills
- Familiarity with cloud compute, storage, network, and services
- Attention to detail and a passion for correctness
Deadline to apply: None. Applications will be reviewed on a rolling basis.
- The annual compensation range for this role is listed below.
- For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
How we're different
- We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
- The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
